Most MBA finance students still treat AI as a smarter Google. The ones pulling ahead use a small, deliberate stack — one tool to structure the model, another to crunch the data, a third to pull cited research, and a fourth to make it presentable. Here's that stack for 2026: ten tools, grouped by the job they do, and exactly when to reach for each.
1. Build and pressure-test the model
ChatGPT (GPT-4o)
Outline a three-statement model or DCF, surface the assumptions you're missing, and have it explain a variance or sensitivity in plain English before your viva.
Microsoft Copilot for Excel
Generate and audit formulas inside the sheet itself, fix broken references, and document what each section does — without leaving Excel.
Best for: DCFs, three-statement models, LBO and sensitivity tables.
2. Crunch and visualise the data
Julius AI
Upload a CSV and ask questions in plain English — regressions, ratios, cohorts and clean charts, no Python required.
ChatGPT Code Interpreter
For heavier work: scenario analysis, statistical tests, and publication-quality charts from messy data.
Power BI
Turn the analysis into an interactive dashboard your professor — or a recruiter — can click through.
Best for: ratio analysis, scenario modelling, dashboards.
3. Research companies and markets — with sources
Perplexity AI
Cited, real-time company and industry data — genuinely better than Google for valuation comps and market sizing, with links you can actually verify.
NotebookLM
Drop in a 200-page annual report or case pack; it answers only from those documents, so you can trust it for a write-up.
Best for: comparable companies, industry sizing, annual-report synthesis.
4. Write it up and present
ChatGPT / Claude
Turn raw analysis into a tight investment memo or one-page executive summary — then ask it to argue the other side so your recommendation holds up.
Gamma / Canva AI
Generate a clean recommendation deck straight from your memo in minutes.
Best for: investment memos, recommendation decks, placement cases.
How to actually use them in an assignment
- Start with structure, not data. Ask ChatGPT to lay out the model, assumptions and formula logic before you touch a single number.
- Pull inputs from cited sources. Use Perplexity for comps and market data, NotebookLM for anything that lives inside a report or case pack.
- Build and analyse. Model in Excel with Copilot; push the dataset through Julius AI or Code Interpreter for the analysis and charts.
- Explain and present. Have the AI translate the numbers into a plain-English recommendation, then build the dashboard or deck.
Each of these tools has a step-by-step finance guide and ready-to-use prompt templates inside SkilledMBA Pro — or browse the full AI tools directory to see what's mapped to your subject.